
from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  return [
      int(1000 * (box[0] / width)   ),
      int(1000 * (box[1] / height)   ),
      int(1000 * (box[2] / width)   ),
      int(1000 * (box[3] / height)   ),
  ]

def a__       (			A_,	A_,	A_ = None   ):






  '''simple docstring'''





  __magic_name__								=			tesseract_config if tesseract_config is not None else """"""

  # apply OCR
  __magic_name__								=			to_pil_image(A_   )
  __magic_name__	,       __magic_name__								=			pil_image.size
  __magic_name__								=			pytesseract.image_to_data(A_,	lang=A_,	output_type="""dict""",	config=A_   )
  __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

  # filter empty words and corresponding coordinates
  __magic_name__								=			[idx for idx, word in enumerate(A_   ) if not word.strip()]
  __magic_name__								=			[word for idx, word in enumerate(A_   ) if idx not in irrelevant_indices]
  __magic_name__								=			[coord for idx, coord in enumerate(A_   ) if idx not in irrelevant_indices]
  __magic_name__								=			[coord for idx, coord in enumerate(A_   ) if idx not in irrelevant_indices]
  __magic_name__								=			[coord for idx, coord in enumerate(A_   ) if idx not in irrelevant_indices]
  __magic_name__								=			[coord for idx, coord in enumerate(A_   ) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  __magic_name__								=			[]
  for x, y, w, h in zip(A_,	A_,	A_,	A_   ):
    __magic_name__								=			[x, y, x + w, y + h]
    actual_boxes.append(A_   )

  # finally, normalize the bounding boxes
  __magic_name__								=			[]
  for box in actual_boxes:
    normalized_boxes.append(normalize_box(A_,	A_,	A_   )   )

  assert len(A_   ) == len(A_   ), "Not as many words as there are bounding boxes"

  return words, normalized_boxes





class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""pixel_values"""]
  def __init__(					self     :					str     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Dict[str, int] = None     ,				UpperCamelCase__     :					PILImageResampling = PILImageResampling.BILINEAR     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[str] = None     ,				UpperCamelCase__     :					Optional[str] = ""     ,				**UpperCamelCase__     :					Union[str, Any]     ,				)     ->      None:







    """simple docstring"""






    super().__init__(**UpperCamelCase__       )
    __magic_name__								=			size if size is not None else {"""height""": 224, """width""": 224}
    __magic_name__								=			get_size_dict(UpperCamelCase__       )

    __magic_name__								=			do_resize
    __magic_name__								=			size
    __magic_name__								=			resample
    __magic_name__								=			apply_ocr
    __magic_name__								=			ocr_lang
    __magic_name__								=			tesseract_config
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					np.ndarray     ,				UpperCamelCase__     :					Dict[str, int]     ,				UpperCamelCase__     :					PILImageResampling = PILImageResampling.BILINEAR     ,				UpperCamelCase__     :					Optional[Union[str, ChannelDimension]] = None     ,				**UpperCamelCase__     :					str     ,				)     ->      np.ndarray:







    """simple docstring"""






    __magic_name__								=			get_size_dict(UpperCamelCase__       )
    if "height" not in size or "width" not in size:
      raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
    __magic_name__								=			(size["""height"""], size["""width"""])
    return resize(UpperCamelCase__     ,				size=UpperCamelCase__     ,				resample=UpperCamelCase__     ,				data_format=UpperCamelCase__     ,				**UpperCamelCase__       )





  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					ImageInput     ,				UpperCamelCase__     :					bool = None     ,				UpperCamelCase__     :					Dict[str, int] = None     ,				UpperCamelCase__     :					PILImageResampling = None     ,				UpperCamelCase__     :					bool = None     ,				UpperCamelCase__     :					Optional[str] = None     ,				UpperCamelCase__     :					Optional[str] = None     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				UpperCamelCase__     :					ChannelDimension = ChannelDimension.FIRST     ,				**UpperCamelCase__     :					str     ,				)     ->      PIL.Image.Image:







    """simple docstring"""






    __magic_name__								=			do_resize if do_resize is not None else self.do_resize
    __magic_name__								=			size if size is not None else self.size
    __magic_name__								=			get_size_dict(UpperCamelCase__       )
    __magic_name__								=			resample if resample is not None else self.resample
    __magic_name__								=			apply_ocr if apply_ocr is not None else self.apply_ocr
    __magic_name__								=			ocr_lang if ocr_lang is not None else self.ocr_lang
    __magic_name__								=			tesseract_config if tesseract_config is not None else self.tesseract_config

    __magic_name__								=			make_list_of_images(UpperCamelCase__       )

    if not valid_images(UpperCamelCase__       ):
      raise ValueError(
          """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
          """torch.Tensor, tf.Tensor or jax.ndarray."""       )

    if do_resize and size is None:
      raise ValueError("""Size must be specified if do_resize is True."""       )

    # All transformations expect numpy arrays.
    __magic_name__								=			[to_numpy_array(UpperCamelCase__       ) for image in images]

    if apply_ocr:
      requires_backends(self     ,				"""pytesseract"""       )
      __magic_name__								=			[]
      __magic_name__								=			[]
      for image in images:
        __magic_name__	,       __magic_name__								=			apply_tesseract(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
        words_batch.append(UpperCamelCase__       )
        boxes_batch.append(UpperCamelCase__       )

    if do_resize:
      __magic_name__								=			[self.resize(image=UpperCamelCase__     ,				size=UpperCamelCase__     ,				resample=UpperCamelCase__       ) for image in images]

    # flip color channels from RGB to BGR (as Detectron2 requires this)
    __magic_name__								=			[flip_channel_order(UpperCamelCase__       ) for image in images]
    __magic_name__								=			[to_channel_dimension_format(UpperCamelCase__     ,				UpperCamelCase__       ) for image in images]

    __magic_name__								=			BatchFeature(data={"""pixel_values""": images}     ,				tensor_type=UpperCamelCase__       )

    if apply_ocr:
      __magic_name__								=			words_batch
      __magic_name__								=			boxes_batch
    return data



import string

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			""""""
  for i in sequence:
    __magic_name__								=			ord(A_   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			string.ascii_letters
  __magic_name__								=			string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(A_   )] if c in letters else c for c in sequence   )

def a__       (			):






  '''simple docstring'''





  from timeit import timeit

  print("""Running performance benchmarks..."""   )
  __magic_name__								=			"""from string import printable ; from __main__ import atbash, atbash_slow"""
  print(f'''> atbash_slow(): {timeit('atbash_slow(printable)',	setup=A_   )} seconds'''   )
  print(f'''>      atbash(): {timeit('atbash(printable)',	setup=A_   )} seconds'''   )


if __name__ == "__main__":
    for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
        print(F'''{example} encrypted in atbash: {atbash(example)}''')
    benchmark()

